Method for processing an image sequence of a distortable 3-d object to yield indications of the object wall deformations in time

ABSTRACT

An image processing method for processing a sequence of images of a distortable 3-D Object, each image being registered at a corresponding image instant within the interval of time of the sequence having steps to construct and display an image of said 3-D Object represented with regions, each region showing a quantified indication relating to its maximal contraction or relaxation within said interval of time. Each region of the constructed and displayed image is attributed a respective color of a color coded scale that is function of the calculated quantified indication relating to the maximum of contraction or relaxation of said region. The quantified indications may be the instant when a face or region has had its maximum of contraction or relaxation; or the phase value corresponding to said maximum; or the delay to attain said maximum.

[0001] The invention relates to a method for processing a sequence ofimages of a distortable 3-D object to yield indications of the objectwall deformations during a given interval of time. In particular, theinvention relates to a method for processing a sequence of 3-Dultrasound images of a body organ having a wall that moves eitherinwardly or outwardly in the time, to yield indications of said organwall motions during contractions or relaxations and other possiblemotions registered within an interval of time in the image sequence.

[0002] The invention particularly finds an application in the field ofthe ultrasound medical apparatus or systems, for processing ultrasound3-D image sequences produced by these apparatus or systems, in order todisplay information of the way the organ walls move over a time delayduring which a sequence of 3-D ultrasound images is registered.

[0003] A technique of modelization of a 3-D object is already disclosedby H. Delingette in the publication entitled “Simplex Meshes: a GeneralRepresentation for 3D shape Reconstruction” published in the“Proceedings of the International Conference on Computer Vision andPattern Recognition (CVPR'94), Jun. 20-24, 1994, Seattle, USA”. In thispaper, a physically based approach for recovering three-dimensionalobjects is presented. This approach is based on the geometry of “SimplexMeshes”. Elastic behavior of the meshes is modeled by local stabilizingfunctions controlling the mean curvature through the simplex angleextracted at each vertex (node of the mesh). Those functions areviewpoint-invariant, intrinsic and scale-sensitive. Unlike distortablesurfaces defined on regular grids, Simplex Meshes are very adaptivestructures. A refinement process for increasing the mesh resolution athighly curved or inaccurate parts is also disclosed. Operations forconnecting Simplex Meshes in order to recover complex models may beperformed using parts having simpler shapes.

[0004] A Simplex Mesh has constant vertex connectivity. For representing3-D surfaces, 2-D Simplex Meshes, where each vertex is connected tothree neighboring vertices, are used. The structure of a Simplex Mesh isdual to the structure of a triangulation as illustrated by the FIG. 1 ofthe cited publication. It can represent all types of rotatable surface.The contour on a Simplex Mesh is defined as a closed polygonal chainconsisting in neighboring vertices on the Simplex Mesh. The contour isrestricted to not intersect itself. Contours are distortable models andare handled in independently of the Simplex Mesh where they areembedded. Four independent transformations are defined for achieving thewhole range of possible mesh transformations. They consist in insertingor in deleting edges in a face. The description of the Simplex Mesh alsocomprises the definition of a Simplex Angle that generalized the angleused in planar geometry; and the definition of metric parameters thatdescribe how the vertex is located with respect to its three neighbors.The dynamic of each vertex is given by a Newtorian law of motion. Thedeformation implies a force that constrains the shape to be smooth and aforce that constrains the mesh to be close to the 3-D data-Internalforces determine the response of a physically based model to externalconstraints. The internal forces are expressed so that they be intrinsicviewpoint invariant and scale dependant. Similar types of constraintshold for contours. Hence, the cited publication provides a simple modelfor representing a 3-D object of interest. It defines the forces to beapplied in order to reshape and adjust the model onto the 3-D object ofinterest. The “Simplex Mesh technique” is a robust segmentationtechnique.

[0005] The invention relates to an image processing method forprocessing a sequence of images of a distortable 3-D Object, each imagebeing registered at a corresponding image instant within the interval oftime of the sequence. The inward motions of the 3-D Object boundary arecalled contractions and the outward motions are called relaxations. Itis an object of the invention to propose such a processing method havingsteps to construct and display an image of said 3-D Object representedwith regions, each region showing a quantified indication relating toits maximal contraction or relaxation within said interval of time.

[0006] Such an image processing method is claimed in claim 1.

[0007] The displayed image provides the advantage to yield an easyestimation of the propagation of the deformations over the 3-D Objectboundary within the given interval of time. In the displayed image, eachregion may show a quantified indication of the phase of a predeterminedperiodic function representing the motion of the region, said phaseindication corresponding to the image of the sequence in which themaximal contraction or relaxation of said region has been estimated. Inthe displayed image, each region may show a quantified indicationcorresponding to the delay necessary for said region to attain itsmaximal contraction or relaxation from a predetermined reference. In thedisplayed image, each region may show a quantified indication of theimage instant when said region have had its maximal contraction orrelaxation between the image corresponding to said image instant and anadjacent image in the sequence.

[0008] It is also an object of the invention to propose such an imageprocessing method for processing a sequence of 3-D ultrasound images ofa body organ having a wall with regions that move either inwardly oroutwardly in the time, in order to construct and display a virtual imageof the organ wall represented with regions, having such quantifiedindications. It is also an object of the invention to propose such animage processing method wherein the quantified indications are given ina coded manner, preferably in a color coded manner. It is particularlyan object of the invention to apply this method to 3-D ultrasoundimaging, in order to yield quantified information relating to themaximum deformation of regions of the heart, in a color coded form, foreasily estimating the propagation of the deformation during contractionand relaxation of cavities of the heart, from a sequence of imagesregistered during the interval of time of a cardiac cycle. So, theinvention also relates to an ultrasound examination apparatus havingimage processing means and to a program product for carrying out themethod.

[0009] The invention is described hereafter in detail in reference tothe following diagrammatic drawings, wherein:

[0010]FIG. 1A shows an ultrasound 3-D image of the heart left ventricleas a distortable object; FIG. 1B shows a 3-D Simplex Mesh Model forsegmenting this object;

[0011]FIG. 2 illustrates the determination of the maximal “distance” fora given region of the object wall, which is found between two imagesduring a given interval of time;

[0012]FIGS. 3A, 3B and 3C illustrate the determination of the “distance”for a given region of the object wall at different instants of asequence of images;

[0013]FIG. 4 is a reproduction in black and white of a color coded imageof a virtual image of the heart displayed according to the inventioncorresponding to the heart motions during a cardiac cycle;

[0014]FIG. 5 shows a diagram of an apparatus for carrying out themethod.

[0015] The invention relates to an image processing method for analyzingthe amplitude and direction of the displacements of wall regions of adistortable 3-D Object over a sequence of images, and for constructingand displaying a virtual image of said 3-D Object represented with theregions, each region showing a quantified indication relating to itsmaximal contraction or relaxation within the interval of time of thesequence of images. Said indications are preferably given in a codedmanner, such as in a color-coded manner. The invention may be applied toa sequence of 3-D ultrasound images of a body organ having a wall withregions that move either inwardly or outwardly in the time, said organhaving nearly periodic motions, in order to construct and display avirtual image of the organ wall represented with regions, having suchquantified indications. The invention is particularly favorably appliedto a sequence of 3-D ultrasound images representing the wall of a heartcavity having motions of contraction and/or relaxation during theinterval of time of a cardiac cycle. Using 3D-ultrasound imaging, thismethod permits of analyzing the contraction and relaxation of the heart.This method yields quantified information relating to the maximumdeformation of regions of a cavity of the heart, in a color coded form,for easily estimating the propagation of local deformation over theheart wall during contraction and relaxation of said cavity, from asequence of images registered during the interval of time of the cardiaccycle.

[0016] The displayed virtual image provides the advantage to yield aneasy estimation of the propagation of the deformations over a 3-D Objectboundary within a given interval of time. In the displayed virtualimage, each region may show a quantified indication of the phase of apredetermined periodic function representing the motion of the region,said phase indication corresponding to the image of the sequence inwhich the maximal contraction or relaxation of said region has beenestimated within said interval of time. Alternately, in the displayedvirtual image, each region may show a quantified indicationcorresponding to the delay necessary for said region to attain itsmaximal contraction or relaxation from a predetermined reference. Inanother embodiment, in the displayed virtual image, each region may showa quantified indication of the image instant within the sequence, whensaid region have had its maximal contraction or relaxation between theimage corresponding to said image instant and an adjacent image of thesequence.

[0017] The quantitative estimation of local cardiac deformations,corresponding to contractions and relaxations of the heart wall regionsrepresented in a 3-D image sequence, has important clinical implicationsfor the assessment of the viability of cardiac muscle cells in saidheart wall. The cardiac contractions and the cardiac relaxations arecomplex spatio-temporal phenomena, activated by the temporal changes ofelectrical potential in the cardiac muscular cells. During contraction,also occurs a twist motion of the heart wall. These considerationsemphasize the complexity of the deformation, that may not be simplydescribed as a temporal radial contraction or relaxation of the heartwall. Hence, a good spatial resolution is required when studying thecontraction or the relaxation. Moreover, not only the amplitude of thecontraction should be studied, but also the phase, which indicateslocally the time when contraction or relaxation happens, and the way itis propagating. There are several cardio-pathologies due to conductiondiseases: tachycardia and atrial or ventricular fibrillation are someexamples. The analysis of the local cardiac contractions providesinformation about the condition of the heart and is useful for the studyof such cardio-pathologies, as well as those inducing conductionabnormalities, such as the myocardial infarction for instance.

[0018] The method can be carried out using reconstructed or real-time 3Dechocardiography, the images being formed using a trans-thoracic or atrans-esophageal probe. The method of the invention can also be appliedto a sequence of 3-D images of other organs of the body that can beformed by ultrasound systems or ultrasound apparatus, or by othermedical imaging systems known of those skilled in the art.

[0019] In the example described hereafter, analysis of the cardiac wallmotion is performed from a sequence of 3-D simplified models of the leftventricular volume, which are obtained from the segmentation of 3-Dultrasound images of the heart. For the construction of the virtualimage to be displayed and for the estimation of quantified indicationsrelating to the contraction or relaxation of regions of the heart to berepresented in said virtual image, the sequence of 3-D segmented imagesis further processed using one of two different techniques or both thosetechniques.

[0020] The first technique consists in a Fourier analysis of the motionover the models of the 3-D segmented sequence. A first model, calledreference model of the left ventricle, is first chosen in an imagecalled first image among the different successive images of the sequenceof 3-D simplified models. The volume of the left ventricle varies fromone image to the following image. So, the corresponding models vary fromone image to the following image. This first technique comprises, foreach region of the virtual image or model to be displayed, a definitionof corresponding region on each model of the image sequence. Then, thisfirst technique comprises a computation of the motion between thecorresponding region defined on each model of the images of the sequenceand the corresponding region on the reference model of the first image,based on the assumption that this motion is periodic. This firsttechnique further comprises a definition of a periodic function ofmotion and a derivation of the phase associated to the motion from aFourier analysis, for estimating a continuous information of phase fromthe set of images forming the sequence. The continuous information ofphase indicates the delay for attaining the maximum of contraction orrelaxation, for each region of the virtual model, from the referencemodel.

[0021] The second technique comprises a computation of the amplitude ofmotion between corresponding regions of two successive models, calledcouple of models, of two successive images of the sequence, instead ofconsidering each model of the sequence with respect to a referencemodel. To each couple of model is associated an instant of time: forinstance, the image instant when the last in time of the two images ofthe couple is registered in the interval of time of the sequence. Inthis second technique, the amplitude of motion called “distance” isestimated for the regions of each couple of models. The image instantwithin the interval of time of the sequence when this motion is maximalcorresponds to a maximum of contraction or relaxation, betweencorresponding regions on the considered models of a couple.

[0022] These two techniques are complementary: the first one gives localquantified information of phase based on a global time-analysis of themotion throughout the sequence and on the assumption that the cardiacmotion is periodic; and the second one gives local quantifiedinformation of the instant of time when a maximum of motion occursbetween two models, so is based on an analysis of the motion that ismore precise in the temporal dimension.

[0023] In order to represent the local quantified information of maximalcontraction or relaxation in the sequence of images, a predefinedcolor-map is used. Said color-map associates the instant of maximalcontraction or relaxation related to each region as estimated accordingto the second technique, or the phase related to each region asestimated according to the first proposed technique, to a color, andthen fits the color on the corresponding regions of a generic or meanmodel, called virtual model, of the left ventricle, thus yielding theinformation of the way the contraction or relaxation propagates in themyocardium. Furthermore, the path of propagation of the contraction orrelaxation can be superimposed on this representation.

[0024] The present image processing method comprises steps of:

[0025] 1) Acquisition of a Sequence of 3-D Images of a 3-D Object.

[0026] In an example, 3-D images of a heart cavity wall, for example thewall of the heart left ventricle, are acquired using an ultrasoundexamination apparatus. FIG. 1A represents one of such images. Theseimages are assembled in a sequence of images. The sequence images can beacquired at a rate of 15 to 30 or 50 images per second, each image ofthe sequence being preferably associated to an instant of the cardiaccycle. Other examples of forming sequences of 3-D images of differentorgans, whose shape or dimensions vary over time, may be found byoperators of ultrasound apparatus or of other systems of imageacquisition.

[0027] 2) Segmentation of the 3-D Images of the Sequence.

[0028] After the acquisition of the image sequence, the images aresegmented. Any method of segmentation, which is able to segment the 3-Dobject in the images of the sequence, may be used. The result of thesegmentation operation permits of locating the voxels of the wall of the3-D Object, for instance the internal wall of the left ventricle.Referring to FIG. 1B, preferably, the segmentation technique of “SimplexMesh” is used because it is robust and gives excellent results. ThisSimplex Mesh Technique has been previously described in relation to thepublication above cited as the state of the art. A “Simplex Mesh model”that is used is illustrated by FIG. 1B. The segmentation step consistsin mapping the Simplex Mesh Model onto the 3-D object of FIG. 1A. In thecase of a heart cavity, an elementary 3-D Mesh Model, which may be forexample a sphere, is set inside the 3-D cavity, then it is deformed andreshaped using the above-described internal and external forces, untilit is mapped unto the internal wall of the cavity. This operation isperformed for each image of the sequence, so that a sequence of imagesrepresenting segmented 3-D objects is formed. In each of these images,the wall of the object of interest is represented by a Simplex MeshModel with faces and edges. The faces are generally not planar. It is tobe noted that in the process of segmentation, the segmented 3-D Objectis represented by the faces and edges of the Mesh Model. The facesdefine regions of the 3-D Object. For refining the mapping of the MeshModel onto the 3-D Object, the faces may be divided. So, the number offaces may differ from a model in a given image to the model in anotherimage of the sequence. In order to avoid difficulties due to a varyingnumber of faces of the models representing the segmented 3-D Object fromone image to another, a unique number of faces corresponding to a givenlevel of segmentation is preferably chosen for all the models of theimages of the sequence, thus defining the number of regions to beconsidered.

[0029] These segmented images may be processed in order to transformeach model representing the 3-D segmented Object into a binary model.For instance, the voxels inside the model are attributed the value 1,the voxels outside the model are attributed the value 0. The boundary ofthe 3-D binary model is located between the 0 and 1 regions andrepresents the location of the organ wall. Other possibility forattributing a boundary to a binary object may be used as known of thoseskilled in the art. The formation of a sequence of binary models isoptional, but permits of minimizing the amount of calculation in thefurther steps of the image processing method.

[0030] In the segmented images, the 3-D Object that has been segmentedusing the Simplex Mesh Model, has faces denoted by Z having a center ofgravity denoted by ZC. The point ZC may alternately be a reference pointof a region of the simplified model.

[0031] 3). Analysis of the Wall Movements for Estimating QuantifiedIndications Relating to Maximal Contraction Or Relaxation of theRegions.

[0032] The local analysis of the heart wall motions during contractionand relaxation is performed using the above cited two differentcomplementary techniques.

[0033] 3.1). Using Fourier Analysis of the Wall Motions.

[0034] The walls of the segmented (or binary) 3-D Objects in the imagesof the sequence define different “volumes” of the models at differentregistered instants. The volume of the models varies periodically forphysiological reasons. In the present example, the volume of the leftventricle varies periodically in function of the pulse during thecardiac cycle. Referring to FIGS. 3A, 3B, 3C, in the first techniqueimplying a Fourier analysis, a method is proposed for the determinationof a “distance” between the walls of the models considered in thesequence of segmented (or binary) images. This information of distanceis defined in order to permit of estimating the amplitudes of movementof each face or region of the model in function of time. Some faces mayshow great amplitudes of movement during the cardiac cycle; some otherfaces may show very small amplitudes of movement during said cardiaccycle; some may have regular amplitudes of movement over several cardiaccycles; other faces may have irregular amplitudes of movement overseveral cardiac cycles. The centers of gravity C₀, C₁, C₂, . . . C_(N),of the models in the segmented images of the sequence are alsoconsidered. Referring to FIG. 3A, in an example, the centers of gravityC₀, C₁ of the models in the first and second segmented images may befound to be located in coincidence or not. If they are not located incoincidence, an operation of translation may be performed to superimposethose points C₀, C₁. In this first technique, the 3-D object ofinterest, for example the heart left ventricle, is first considered inone segmented (or binary) image of the sequence called first segmentedimage. Once the first segmented image is chosen, the other segmented (orbinary) images of the sequence where the left ventricle varies in shapeand dimension during the cardiac cycle are further considered one byone. Referring to FIG. 3A, the wall of the first segmented (or binary)3-D Object defines a first cavity volume called first “volume” V₀ of themodel at a first instant. Referring to FIG. 3B, the wall of the secondsegmented (or binary) 3-D Object defines a second cavity volume called“volume” V₁ of the model at a second instant. In this first technique,two processes are proposed, as examples, for obtaining the informationof motion, during the cardiac cycle, of the regions that have beendefined and delineated on the virtual image.

[0035] In a first process, corresponding regions are selected on thedifferent models, these regions also corresponding to the regions of thevirtual image. The “distance” is then defined as the computed distancebetween each region of the model and the common center. Referring toFIG. 3A, the “distance” from each region to the common center is firstcalculated for the first volume V₀. This distance is denoted by D₀.Then, referring to FIG. 3B, the “distance” from each face to the commoncenter is calculated for the second volume V₁. This distance is denotedby D₁. Referring to FIG. 3C, the “distances” from each face to thecommon center are calculated for all the volumes of the sequence ofsegmented (or binary) 3-D Objects. A simple method to estimate the“distance” of a face or a region to the common center is to calculatethe distance between the reference point of the face or region and thecommon center.

[0036] In a second process, the different models are all binary models.The centers of gravity of these models and of the virtual model may besuperimposed forming a common center of gravity. Then, as illustrated byFIGS. 3A to 3C, for each region of the virtual image, the distancesrelating to the different binary models may be calculated along a linejoining the center of gravity of the considered region on the virtualmodel to the center of gravity of said virtual model, which is thecommon center of gravity.

[0037] A function called function of motion is further estimated fromthese calculated distances. Assuming that the variation of saiddistances for the corresponding regions of the different volumes isperiodic, the estimated function is a periodic function. For each faceor region of a model, the phase of said function is derived from aFourier analysis. Then a continuous information of phase is estimatedover the corresponding faces or regions from the set of images formingthe image sequence. This continuous information of phase permits ofestimating the delay to attain the maximal contraction or relaxation foreach face or for each region of the Models. The phase may be estimatedin degrees or grades or radians. The delay may be estimated in unity oftime or in function of the instant of image acquisition in the sequence.In this technique of phase calculation, the information of phase maythus be used to estimate the instant of the maximum of contraction orthe maximum of relaxation of the heart left ventricle. This technique ofFourier analysis, based on a global time-analysis of the motionthroughout the sequence, gives an information based on the assumptionthat the cardiac motion is periodic.

[0038] 3.2). Using the Amplitude of Motion Between Two SuccessiveModels.

[0039] Referring to FIG. 2, once the points C₀, C₁ are superimposed, thedisplacement of the wall of the second model with respect to the firstmodel, between the instants of the first and the second images, gives a“distance” denoted by D′₁ between the boundary of the first volume V₀and the boundary of the second volume V₁ measured along a line issuedfrom the common center of gravity C₀, C₁ of the first and second volumesV₀, V₁ and joining a reference point of a predefined region or face ofthe second volume. The displacement of the wall of the next volume V₂with respect to the second volume V₁ gives the distance D′₂ between theboundary of said second volume V₁ and the boundary of the third volumeV₂ measured along the same line issued from the common center of gravityC₀. In this technique, the different distances between the differentmodels of the different segmented images are calculated. The models areconsidered by two, thus forming couples between the corresponding facesor regions of which the “distances” are calculated. The information ofthe distance between two successive volumes is used for computing theamplitude of motion between these two volumes. The instant of the imagesequence when this amplitude of motion corresponds to a maximum D′_(MAX)or to a minimum of volume corresponds to a maximum of contraction or ofrelaxation. This technique permits of analyzing the motion moreprecisely in the temporal dimension.

[0040] An interesting feature is to use this information of amplitudesfor determining the smallest “volume” or the greatest “volume” of thisheart ventricle and the corresponding instant of the cardiac cycle.

[0041] 4). Constructing an Image of a Virtual Segmented Object

[0042] An image of the virtual model is constructed having a givennumber of faces or regions, which may correspond to the faces or regionsof the successive models of the sequence, providing a predeterminedlevel of segmentation of the models as above described.

[0043] 5) Associating Given Colors to the Quantified IndicationsRelating to Maximal Motion.

[0044] Referring to FIG. 4, in this step, a Table of Colors associates acolor to the quantified indications calculated from the sequence ofsuccessive simplified models. Using this color-coding operation,different colors may be associated to the different image instants ofthe sequence registered during a cardiac cycle (in unity of time or intime divisions of the cardiac cycle); or the values of phase (in degree,grade or radian); or the delays of time (in unity of time or in timedivisions of the cardiac cycle). Numerous other color-coding techniquesmay be used by those skilled in the art for performing this step. FIG. 4represent the color-coded object in shades of coded gray scale.

[0045] 6) Displaying an Image of a Color-Coded Virtual Model

[0046] Using the color-coded Map defined by the color-coding operation,the appropriate colors, corresponding to the quantified indications ofthe maximum of contraction or relaxation, are fitted to the faces orregions of the virtual model, in order that each region or face berepresented with a quantified indication as above calculated either withthe first or the second technique. These indications may be the instantwhen a face or region has had its maximum of contraction or relaxationas measured in the second technique; or the phase value corresponding toits maximum of contraction or relaxation as measured in the firstproposed technique; or the delay to attain its maximum of contraction orrelaxation as measured in the first proposed technique.

[0047] This operation of coloring the faces or regions in function ofthe color-coded Map yields the information of the way the contraction orrelaxation propagates in the myocardium. Furthermore, the path ofpropagation of the contraction or relaxation can be superimposed on thisrepresentation. Referring to FIG. 4, this operation provides a virtualcolor-coded image. Each face or each zone Z of the model representingthe object of interest is attributed a color specific of the quantifiedindications. The color-coded virtual image is displayed for example on ascreen. It may be registered or memorized. Preferably, a scale of colorsrepresenting the used quantified indication, such as instant of time,phase, delay, is displayed together with the virtual model. This permitsa doctor of estimating the propagation of movement over the cardiac wallduring a cardiac cycle. In the example of the left ventriclerepresentation, it is favorable to display a curve of the cardiac pulsevariation during cardiac cycles.

[0048] This method can be applied without difficulty to 2-D images,which are for instance cross-sections of the 3-D images of a 3-D object.In the case of the simplex mesh segmentation method, the 2-D segmentedobjects of the sequence are polygons having edges that are colored infunction of their distances to the boundary of a 2-D object ofreference. For a 3-D sequence of images, one might provide threeorthogonal cross-section image sequences. When other segmentationmethods are used, the 2-D images represent the trace of the wall of thesegmented 3-D object. In these 2-D images, the wall has colored pixelsor colored parts whose colors are function of their measured distanceswith respect to the boundary of the object of reference. The 3D or the2D methods may be applied to ultrasound images as well as to X-rayimages or to any other kind of image sequences.

[0049] Referring to FIG. 5, a medical examination apparatus 150comprises means for acquiring a digital image sequence, and a digitalprocessing system 120 for processing these data according to theprocessing method above-described. The examination apparatus comprisesmeans for providing image data to the processing system 120 which has atleast one output 106 to provide image data to display and/or storagemeans 130, 140. The display and storage means may respectively be thescreen 140 and the memory 130 of a workstation 110. Said storage meansmay be alternately external storage means. This image processing system120 may be a suitably programmed computer of the workstation 110, or aspecial purpose processor having circuit means such as LUTs, Memories,Filters, Logic Operators, that are arranged to perform the functions ofthe method steps according to the invention. The workstation 110 mayalso comprise a keyboard 131 and a mouse 132. This medical examinationapparatus 150 may be a standard ultrasonic apparatus. The processingsystem 120 may use a computer program product having programinstructions to be executed by the computing means of said processingsystem in order to carry out the above-described method.

1. An image processing method for processing a sequence of images of adistortable 3-D Object, each image being registered at a correspondingimage instant within the interval of time of the sequence having stepsto construct and display an image of said 3-D Object represented withregions, each region showing a quantified indication relating to itsmaximal contraction or relaxation within said interval of time.
 2. Theimage processing method of claim 1, wherein a quantified indication isthe instant of the sequence when a region has had its maximum ofcontraction or relaxation between two successive images.
 3. The imageprocessing method of claim 2, wherein the amplitudes of motion betweensuccessive corresponding regions of the 3-D Object throughout thesequence of images are calculated as distances; and the maximal distanceis detected for each set of successive regions; and the instant of thesequence when said maximal distance occurs is identified as thequantified indication of the instant of the sequence when a region hashad its maximum of contraction or relaxation between two successiveimages.
 4. The image processing method of claim 1, wherein a quantifiedindication is the phase value corresponding to the maximum ofcontraction or relaxation of a region.
 5. The image processing method ofclaim 4, wherein the amplitudes of motion between the correspondingregions in the successive images and a common reference point predefinedfor all the images are calculated as distances; periodic functions ofmotion are estimated from these distances for the corresponding regionsin the successive images; deriving phase values from Fourier analysis ofsaid functions; identifying phase values of the periodic functions ofmotion as the quantified indication of phase relating to the maximum ofcontraction or relaxation for the corresponding regions of the 3-DObject in the images of the sequence.
 6. The image processing method ofclaim 5, wherein the distances between a region and the common referencepoint comprises a computation of the distance between a reference pointof the region and a common center of gravity of the 3-D objectrepresented throughout the sequence.
 7. The image processing method ofclaim 1, wherein a quantified indication is the delay for a region toattain its maximum of contraction or relaxation.
 8. The image processingmethod of one of claims 5 or 6, wherein a quantified indication is thedelay for a region to attain its maximum of contraction or relaxation;and wherein for calculating said indication, a continuous information ofphase is derived from each motion function and delay is estimated fromsaid continuous information of phase.
 9. The image processing method ofany one of claims 1 to 8, wherein each region of the constructed anddisplayed image is attributed a respective color of a color coded scalethat is function of the calculated quantified indication relating to themaximum of contraction or relaxation of said region.
 10. The imageprocessing method of one of claims 1 to 9, comprising steps of acquiringimage data of an image sequence, segmenting the 3-D object in the imagesof the sequence for locating the wall of the 3-D object, and coloringregions of the wall in function of the calculated quantified indicationsrelating to the maximum of contraction or relaxation of said regions.11. The image processing method of claim 10, wherein the segmented 3-Dobject in the sequence is processed for providing binary objects havinga boundary representing the wall of the 3-D object of reference.
 12. Theimage processing method of one of claims 10 or 11, wherein the 3-Dobject wall in the segmented images of the sequence are represented by aset of corresponding faces or regions.
 13. The image processing methodof one of claims 1 to 12, wherein 2-D images are formed representingcross-sections of the 3-D object in the sequence, and an image of thesegmented 3-D Object is constructed and displayed having colored partswhose colors are function of the calculated quantified indicationrelating to the maximum of contraction or relaxation of said region. 14.The image processing method of any one of the previous claims, whereinthe 3-D Object is the heart left ventricle.
 15. The image processingmethod of any one of the previous claims, wherein the color-coded 3-Dobject is displayed in a 2-D or of 3-D image.
 16. A system comprising asuitably programmed computer or a special purpose processor havingcircuit means, which are arranged to process image data according to themethod as claimed in any of claims 1 to
 15. 17. An apparatus havingmeans to acquire medical image data, having a system as claimed in claim16 having access to said medical digital image data, and processing theimage data, and having means to display the constructed virtual image.18. An ultrasound examination apparatus having means for carrying out animage processing method as claimed in one of claims 1 to 15, havingmeans for processing a sequence of images of a distortable 3-D Object toconstruct and display an image of said 3-D Object represented withregions, each region showing a quantified indication relating to itsmaximal contraction or relaxation within the interval of time of thesequence.
 19. A computer program product comprising a set ofinstructions for carrying out a method as claimed in one of claims 1 to15.